| Challenge: | Existing methods for classification of labels are limited by feature aggregation and encoding. |
| Approach: | They propose to use hyperbolic capsule networks to capture fine-grained label information . they also propose a new routing method to adaptively adjust capsule number during routing . |
| Outcome: | The proposed method significantly improves the performance of multi-label classification on tail labels. |
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Hierarchical Multi-label Classification of Text with Capsule Networks (P19-2)
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| Challenge: | In hierarchical multi-label classification, samples are classified into one or multiple class labels organized in a structured label hierarchy. |
| Approach: | They apply and compare shallow capsule networks for hierarchical multi-label text classification and introduce a new real-world scenario dataset. |
| Outcome: | The proposed model outperforms neural networks and non-neural network architectures on a real-world scenario dataset. |
A Fully Hyperbolic Neural Model for Hierarchical Multi-Class Classification (2020.findings-emnlp)
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| Challenge: | Existing models for fine-grained entity typing have a hierarchical structure . prior work has integrated only explicit hierarchic information by formulating a hierarchy-aware loss or by representing instances and labels in a joint Euclidean embedding space. |
| Approach: | They propose a fully hyperbolic model for multi-class multi-label classification that performs all operations in hyperbolical space. |
| Outcome: | The proposed model performs all operations in hyperbolic space on two challenging datasets and shows it is comparable to state-of-the-art methods on fine-grained classification with remarkable reduction of parameter size. |
Joint Learning of Hyperbolic Label Embeddings for Hierarchical Multi-label Classification (2021.eacl-main)
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| Challenge: | Existing methods for hierarchical multi-label classification do not assume label hierarchy exists. |
| Approach: | They propose to jointly learn the classifier parameters as well as the label embeddings . they propose to use hyperbolic embeddables to gain better generalisation over the labels . |
| Outcome: | The proposed method achieves state-of-the-art generalization on benchmarks and is more accurate than existing methods. |
Investigating Capsule Networks with Dynamic Routing for Text Classification (D18-1)
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| Challenge: | Earlier efforts in text modeling have achieved limited success on word meanings . convolutional neural networks (CNNs) are used to model higher level concepts and facts in texts . |
| Approach: | They propose three strategies to stabilize dynamic routing process to alleviate disturbance of noise capsules. |
| Outcome: | The proposed methods achieve state-of-the-art on 4 out of 6 datasets . they show that capsule networks exhibit significant improvement over baseline methods . |
Investigating Capsule Network and Semantic Feature on Hyperplanes for Text Classification (D19-1)
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| Challenge: | Various neural networks are designed for text classification on the basis of word embedding, but polysemy is a fundamental feature of the natural language, which brings challenges to text classification. |
| Approach: | They propose to use capsule networks to construct the vectorized representation of semantics and utilize hyperplanes to decompose each capsule to acquire the specific senses. |
| Outcome: | The proposed model extracts more discriminative semantic features and yields significant performance gain compared to baseline methods. |
Label-Aware Hyperbolic Embeddings for Fine-grained Emotion Classification (2023.acl-long)
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| Challenge: | Existing models only address text classification problem in the euclidean space, which is not optimal . e.g., fear and terrified labels may not be differentiated in such space, harming performance . |
| Approach: | They propose a framework that can integrate hyperbolic embeddings to improve the task . they learn label embeddements in the hyperbolical space and then add them to the framework . |
| Outcome: | The proposed framework improves fine-grained emotion classification on two benchmark datasets with 3% improvement over previous state-of-the-art models. |
Effective Convolutional Attention Network for Multi-label Clinical Document Classification (2021.emnlp-main)
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| Challenge: | a large number of medical encounters need to be coded everyday due to long document sets and large label set. |
| Approach: | They propose a convolutional attention network for multi-label document classification problem . they use convolution-based encoders and convolution networks to aggregate information across documents . |
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Prototypical Extreme Multi-label Classification with a Dynamic Margin Loss (2025.naacl-long)
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| Challenge: | Recent work in XMC addresses this problem using deep encoders that project text descriptions to an embedding space suitable for recovering the closest labels. |
| Approach: | They propose a method that uses a shallow transformer encoder to combine text-based embeddings, label centroids and learnable free vectors to improve XMC efficiency. |
| Outcome: | The proposed method achieves state-of-the-art in several public benchmarks of different sizes and domains while keeping the model efficient. |
Label-Specific Document Representation for Multi-Label Text Classification (D19-1)
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| Challenge: | Existing methods to classify documents using labels only assign one label to document . multi-label text classification is a challenging task because of the huge amount of documents, words and labels. |
| Approach: | They propose a Label-Specific Attention Network (LSAN) to learn a label-specific document representation. |
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Can Large Language Models Serve as Effective Classifiers for Hierarchical Multi-Label Classification of Scientific Documents at Industrial Scale? (2025.coling-industry)
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| Challenge: | Large Language Models (LLMs) have demonstrated great potential in complex tasks such as multi-label classification, but the vast number of labels can exceed LLMs’ input limits. |
| Approach: | They propose a method that integrates large language models with dense retrieval techniques to overcome these challenges. |
| Outcome: | The proposed methods avoid frequent retraining by leveraging zero-shot and few-shot learning for real-time label assignment. |